{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3ea857b1",
   "metadata": {},
   "source": [
    "# Using local models\n",
    "\n",
    "The popularity of projects like [PrivateGPT](https://github.com/imartinez/privateGPT), [llama.cpp](https://github.com/ggerganov/llama.cpp), [GPT4All](https://github.com/nomic-ai/gpt4all), and [llamafile](https://github.com/Mozilla-Ocho/llamafile) underscore the importance of running LLMs locally.\n",
    "\n",
    "LangChain has [integrations](https://integrations.langchain.com/) with many open-source LLMs that can be run locally.\n",
    "\n",
    "See [here](/docs/guides/local_llms) for setup instructions for these LLMs. \n",
    "\n",
    "For example, here we show how to run `GPT4All` or `LLaMA2` locally (e.g., on your laptop) using local embeddings and a local LLM.\n",
    "\n",
    "## Document Loading \n",
    "\n",
    "First, install packages needed for local embeddings and vector storage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a7dc1ec5",
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install --upgrade --quiet  gigachain gigachain-community langchainhub gpt4all chromadb "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e7543fa",
   "metadata": {},
   "source": [
    "Load and split an example document.\n",
    "\n",
    "We'll use a blog post on agents as an example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f8cf5765",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_community.document_loaders import WebBaseLoader\n",
    "from langchain_text_splitters import RecursiveCharacterTextSplitter\n",
    "\n",
    "loader = WebBaseLoader(\"https://lilianweng.github.io/posts/2023-06-23-agent/\")\n",
    "data = loader.load()\n",
    "\n",
    "text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)\n",
    "all_splits = text_splitter.split_documents(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "131d5059",
   "metadata": {},
   "source": [
    "Next, the below steps will download the `GPT4All` embeddings locally (if you don't already have them)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fdce8923",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_community.embeddings import GPT4AllEmbeddings\n",
    "from langchain_community.vectorstores import Chroma\n",
    "\n",
    "vectorstore = Chroma.from_documents(documents=all_splits, embedding=GPT4AllEmbeddings())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29137915",
   "metadata": {},
   "source": [
    "Test similarity search is working with our local embeddings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b0c55e98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "question = \"What are the approaches to Task Decomposition?\"\n",
    "docs = vectorstore.similarity_search(question)\n",
    "len(docs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "32b43339",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Document(page_content='Task decomposition can be done (1) by LLM with simple prompting like \"Steps for XYZ.\\\\n1.\", \"What are the subgoals for achieving XYZ?\", (2) by using task-specific instructions; e.g. \"Write a story outline.\" for writing a novel, or (3) with human inputs.', metadata={'description': 'Building agents with LLM (large language model) as its core controller is a cool concept. Several proof-of-concepts demos, such as AutoGPT, GPT-Engineer and BabyAGI, serve as inspiring examples. The potentiality of LLM extends beyond generating well-written copies, stories, essays and programs; it can be framed as a powerful general problem solver.\\nAgent System Overview In a LLM-powered autonomous agent system, LLM functions as the agent’s brain, complemented by several key components:', 'language': 'en', 'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'title': \"LLM Powered Autonomous Agents | Lil'Log\"})"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "docs[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "557cd9b8",
   "metadata": {},
   "source": [
    "## Model \n",
    "\n",
    "### Llama-v2\n",
    "\n",
    "If you have an existing GGML model, see [here](/docs/integrations/llms/llamacpp) for instructions for conversion for GGUF. \n",
    "   \n",
    "And / or, you can download a GGUF converted model (e.g., [here](https://huggingface.co/TheBloke)).\n",
    "\n",
    "Finally, as noted in detail [here](/docs/guides/local_llms) install `llama-cpp-python`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9f218576",
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install --upgrade --quiet  llama-cpp-python"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0dd1804f",
   "metadata": {},
   "source": [
    "To enable use of GPU on Apple Silicon, follow the steps [here](https://github.com/abetlen/llama-cpp-python/blob/main/docs/install/macos.md) to use the Python binding `with Metal support`.\n",
    "\n",
    "In particular, ensure that `conda` is using the correct virtual environment that you created (`miniforge3`).\n",
    "\n",
    "E.g., for me:\n",
    "\n",
    "```\n",
    "conda activate /Users/rlm/miniforge3/envs/llama\n",
    "```\n",
    "\n",
    "With this confirmed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2fd6fe25",
   "metadata": {},
   "outputs": [],
   "source": [
    "! CMAKE_ARGS=\"-DLLAMA_METAL=on\" FORCE_CMAKE=1 pip install -U llama-cpp-python --no-cache-dir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cd7164e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_community.llms import LlamaCpp"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcf81052",
   "metadata": {},
   "source": [
    "Setting model parameters as noted in the [llama.cpp docs](/docs/integrations/llms/llamacpp)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "af1176bb-d52a-4cf0-b983-8b7433d45b4f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "llama.cpp: loading model from /Users/rlm/Desktop/Code/llama.cpp/llama-2-13b-chat.ggmlv3.q4_0.bin\n",
      "llama_model_load_internal: format     = ggjt v3 (latest)\n",
      "llama_model_load_internal: n_vocab    = 32000\n",
      "llama_model_load_internal: n_ctx      = 2048\n",
      "llama_model_load_internal: n_embd     = 5120\n",
      "llama_model_load_internal: n_mult     = 256\n",
      "llama_model_load_internal: n_head     = 40\n",
      "llama_model_load_internal: n_layer    = 40\n",
      "llama_model_load_internal: n_rot      = 128\n",
      "llama_model_load_internal: freq_base  = 10000.0\n",
      "llama_model_load_internal: freq_scale = 1\n",
      "llama_model_load_internal: ftype      = 2 (mostly Q4_0)\n",
      "llama_model_load_internal: n_ff       = 13824\n",
      "llama_model_load_internal: model size = 13B\n",
      "llama_model_load_internal: ggml ctx size =    0.09 MB\n",
      "llama_model_load_internal: mem required  = 8819.71 MB (+ 1608.00 MB per state)\n",
      "llama_new_context_with_model: kv self size  = 1600.00 MB\n",
      "ggml_metal_init: allocating\n",
      "ggml_metal_init: using MPS\n",
      "ggml_metal_init: loading '/Users/rlm/miniforge3/envs/llama/lib/python3.9/site-packages/llama_cpp/ggml-metal.metal'\n",
      "ggml_metal_init: loaded kernel_add                            0x76add7460\n",
      "ggml_metal_init: loaded kernel_mul                            0x76add5090\n",
      "ggml_metal_init: loaded kernel_mul_row                        0x76addae00\n",
      "ggml_metal_init: loaded kernel_scale                          0x76adb2940\n",
      "ggml_metal_init: loaded kernel_silu                           0x76adb8610\n",
      "ggml_metal_init: loaded kernel_relu                           0x76addb700\n",
      "ggml_metal_init: loaded kernel_gelu                           0x76addc100\n",
      "ggml_metal_init: loaded kernel_soft_max                       0x76addcb80\n",
      "ggml_metal_init: loaded kernel_diag_mask_inf                  0x76addd600\n",
      "ggml_metal_init: loaded kernel_get_rows_f16                   0x295f16380\n",
      "ggml_metal_init: loaded kernel_get_rows_q4_0                  0x295f165e0\n",
      "ggml_metal_init: loaded kernel_get_rows_q4_1                  0x295f16840\n",
      "ggml_metal_init: loaded kernel_get_rows_q2_K                  0x295f16aa0\n",
      "ggml_metal_init: loaded kernel_get_rows_q3_K                  0x295f16d00\n",
      "ggml_metal_init: loaded kernel_get_rows_q4_K                  0x295f16f60\n",
      "ggml_metal_init: loaded kernel_get_rows_q5_K                  0x295f171c0\n",
      "ggml_metal_init: loaded kernel_get_rows_q6_K                  0x295f17420\n",
      "ggml_metal_init: loaded kernel_rms_norm                       0x295f17680\n",
      "ggml_metal_init: loaded kernel_norm                           0x295f178e0\n",
      "ggml_metal_init: loaded kernel_mul_mat_f16_f32                0x295f17b40\n",
      "ggml_metal_init: loaded kernel_mul_mat_q4_0_f32               0x295f17da0\n",
      "ggml_metal_init: loaded kernel_mul_mat_q4_1_f32               0x295f18000\n",
      "ggml_metal_init: loaded kernel_mul_mat_q2_K_f32               0x7962b9900\n",
      "ggml_metal_init: loaded kernel_mul_mat_q3_K_f32               0x7962bf5f0\n",
      "ggml_metal_init: loaded kernel_mul_mat_q4_K_f32               0x7962bc630\n",
      "ggml_metal_init: loaded kernel_mul_mat_q5_K_f32               0x142045960\n",
      "ggml_metal_init: loaded kernel_mul_mat_q6_K_f32               0x7962ba2b0\n",
      "ggml_metal_init: loaded kernel_rope                           0x7962c35f0\n",
      "ggml_metal_init: loaded kernel_alibi_f32                      0x7962c30b0\n",
      "ggml_metal_init: loaded kernel_cpy_f32_f16                    0x7962c15b0\n",
      "ggml_metal_init: loaded kernel_cpy_f32_f32                    0x7962beb10\n",
      "ggml_metal_init: loaded kernel_cpy_f16_f16                    0x7962bf060\n",
      "ggml_metal_init: recommendedMaxWorkingSetSize = 21845.34 MB\n",
      "ggml_metal_init: hasUnifiedMemory             = true\n",
      "ggml_metal_init: maxTransferRate              = built-in GPU\n",
      "ggml_metal_add_buffer: allocated 'data            ' buffer, size =  6984.06 MB, (35852.94 / 21845.34), warning: current allocated size is greater than the recommended max working set size\n",
      "ggml_metal_add_buffer: allocated 'eval            ' buffer, size =  1026.00 MB, (36878.94 / 21845.34), warning: current allocated size is greater than the recommended max working set size\n",
      "ggml_metal_add_buffer: allocated 'kv              ' buffer, size =  1602.00 MB, (38480.94 / 21845.34), warning: current allocated size is greater than the recommended max working set size\n",
      "ggml_metal_add_buffer: allocated 'scr0            ' buffer, size =   298.00 MB, (38778.94 / 21845.34), warning: current allocated size is greater than the recommended max working set size\n",
      "AVX = 0 | AVX2 = 0 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 0 | NEON = 1 | ARM_FMA = 1 | F16C = 0 | FP16_VA = 1 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 0 | VSX = 0 | \n",
      "ggml_metal_add_buffer: allocated 'scr1            ' buffer, size =   512.00 MB, (39290.94 / 21845.34), warning: current allocated size is greater than the recommended max working set size\n"
     ]
    }
   ],
   "source": [
    "n_gpu_layers = 1  # Metal set to 1 is enough.\n",
    "n_batch = 512  # Should be between 1 and n_ctx, consider the amount of RAM of your Apple Silicon Chip.\n",
    "\n",
    "# Make sure the model path is correct for your system!\n",
    "llm = LlamaCpp(\n",
    "    model_path=\"/Users/rlm/Desktop/Code/llama.cpp/llama-2-13b-chat.ggmlv3.q4_0.bin\",\n",
    "    n_gpu_layers=n_gpu_layers,\n",
    "    n_batch=n_batch,\n",
    "    n_ctx=2048,\n",
    "    f16_kv=True,  # MUST set to True, otherwise you will run into problem after a couple of calls\n",
    "    verbose=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3831b16a",
   "metadata": {},
   "source": [
    "Note that these indicate that [Metal was enabled properly](/docs/integrations/llms/llamacpp):\n",
    "\n",
    "```\n",
    "ggml_metal_init: allocating\n",
    "ggml_metal_init: using MPS\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "e940de71",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Llama.generate: prefix-match hit\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Setting: The Late Show with Stephen Colbert. The studio audience is filled with fans of both comedians, and the energy is electric. The two comedians are seated at a table, ready to begin their epic rap battle.\n",
      "\n",
      "Stephen Colbert: (smirking) Oh, you think you can take me down, John? You're just a Brit with a funny accent, and I'm the king of comedy!\n",
      "John Oliver: (grinning) Oh, you think you're tough, Stephen? You're just a has-been from South Carolina, and I'm the future of comedy!\n",
      "The battle begins, with each comedian delivering clever rhymes and witty insults. Here are a few lines that might be included:\n",
      "Stephen Colbert: (rapping) You may have a big brain, John, but you can't touch my charm / I've got the audience in stitches, while you're just a blemish on the screen / Your accent is so thick, it's like trying to hear a speech through a mouthful of marshmallows / You may have"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "llama_print_timings:        load time =  2201.54 ms\n",
      "llama_print_timings:      sample time =   182.54 ms /   256 runs   (    0.71 ms per token,  1402.41 tokens per second)\n",
      "llama_print_timings: prompt eval time =     0.00 ms /     1 tokens (    0.00 ms per token,      inf tokens per second)\n",
      "llama_print_timings:        eval time =  8484.62 ms /   256 runs   (   33.14 ms per token,    30.17 tokens per second)\n",
      "llama_print_timings:       total time =  9000.62 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\"\\nSetting: The Late Show with Stephen Colbert. The studio audience is filled with fans of both comedians, and the energy is electric. The two comedians are seated at a table, ready to begin their epic rap battle.\\n\\nStephen Colbert: (smirking) Oh, you think you can take me down, John? You're just a Brit with a funny accent, and I'm the king of comedy!\\nJohn Oliver: (grinning) Oh, you think you're tough, Stephen? You're just a has-been from South Carolina, and I'm the future of comedy!\\nThe battle begins, with each comedian delivering clever rhymes and witty insults. Here are a few lines that might be included:\\nStephen Colbert: (rapping) You may have a big brain, John, but you can't touch my charm / I've got the audience in stitches, while you're just a blemish on the screen / Your accent is so thick, it's like trying to hear a speech through a mouthful of marshmallows / You may have\""
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "llm.invoke(\"Simulate a rap battle between Stephen Colbert and John Oliver\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d9579a7",
   "metadata": {},
   "source": [
    "### GPT4All\n",
    "\n",
    "Similarly, we can use `GPT4All`.\n",
    "\n",
    "[Download the GPT4All model binary](/docs/integrations/llms/gpt4all).\n",
    "\n",
    "The Model Explorer on the [GPT4All](https://gpt4all.io/index.html) is a great way to choose and download a model.\n",
    "\n",
    "Then, specify the path that you downloaded to to.\n",
    "\n",
    "E.g., for me, the model lives here:\n",
    "\n",
    "`/Users/rlm/Desktop/Code/gpt4all/models/nous-hermes-13b.ggmlv3.q4_0.bin`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4a24eef1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found model file at  /Users/rlm/Desktop/Code/gpt4all/models/nous-hermes-13b.ggmlv3.q4_0.bin\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "objc[47842]: Class GGMLMetalClass is implemented in both /Users/rlm/anaconda3/envs/lcn2/lib/python3.9/site-packages/gpt4all/llmodel_DO_NOT_MODIFY/build/libreplit-mainline-metal.dylib (0x29f48c208) and /Users/rlm/anaconda3/envs/lcn2/lib/python3.9/site-packages/gpt4all/llmodel_DO_NOT_MODIFY/build/libllamamodel-mainline-metal.dylib (0x29f970208). One of the two will be used. Which one is undefined.\n",
      "llama.cpp: using Metal\n",
      "llama.cpp: loading model from /Users/rlm/Desktop/Code/gpt4all/models/nous-hermes-13b.ggmlv3.q4_0.bin\n",
      "llama_model_load_internal: format     = ggjt v3 (latest)\n",
      "llama_model_load_internal: n_vocab    = 32001\n",
      "llama_model_load_internal: n_ctx      = 2048\n",
      "llama_model_load_internal: n_embd     = 5120\n",
      "llama_model_load_internal: n_mult     = 256\n",
      "llama_model_load_internal: n_head     = 40\n",
      "llama_model_load_internal: n_layer    = 40\n",
      "llama_model_load_internal: n_rot      = 128\n",
      "llama_model_load_internal: ftype      = 2 (mostly Q4_0)\n",
      "llama_model_load_internal: n_ff       = 13824\n",
      "llama_model_load_internal: n_parts    = 1\n",
      "llama_model_load_internal: model size = 13B\n",
      "llama_model_load_internal: ggml ctx size =    0.09 MB\n",
      "llama_model_load_internal: mem required  = 9031.71 MB (+ 1608.00 MB per state)\n",
      "llama_new_context_with_model: kv self size  = 1600.00 MB\n",
      "ggml_metal_init: allocating\n",
      "ggml_metal_init: using MPS\n",
      "ggml_metal_init: loading '/Users/rlm/anaconda3/envs/lcn2/lib/python3.9/site-packages/gpt4all/llmodel_DO_NOT_MODIFY/build/ggml-metal.metal'\n",
      "ggml_metal_init: loaded kernel_add                            0x115fcbfb0\n",
      "ggml_metal_init: loaded kernel_mul                            0x115fcd4a0\n",
      "ggml_metal_init: loaded kernel_mul_row                        0x115fce850\n",
      "ggml_metal_init: loaded kernel_scale                          0x115fcd700\n",
      "ggml_metal_init: loaded kernel_silu                           0x115fcd960\n",
      "ggml_metal_init: loaded kernel_relu                           0x115fcfd50\n",
      "ggml_metal_init: loaded kernel_gelu                           0x115fd03c0\n",
      "ggml_metal_init: loaded kernel_soft_max                       0x115fcf640\n",
      "ggml_metal_init: loaded kernel_diag_mask_inf                  0x115fd07f0\n",
      "ggml_metal_init: loaded kernel_get_rows_f16                   0x1147b2450\n",
      "ggml_metal_init: loaded kernel_get_rows_q4_0                  0x11479d1d0\n",
      "ggml_metal_init: loaded kernel_get_rows_q4_1                  0x1147ad1f0\n",
      "ggml_metal_init: loaded kernel_get_rows_q2_k                  0x1147aef50\n",
      "ggml_metal_init: loaded kernel_get_rows_q3_k                  0x1147af1b0\n",
      "ggml_metal_init: loaded kernel_get_rows_q4_k                  0x1147af410\n",
      "ggml_metal_init: loaded kernel_get_rows_q5_k                  0x1147affa0\n",
      "ggml_metal_init: loaded kernel_get_rows_q6_k                  0x1147b0200\n",
      "ggml_metal_init: loaded kernel_rms_norm                       0x1147b0460\n",
      "ggml_metal_init: loaded kernel_norm                           0x1147bfc90\n",
      "ggml_metal_init: loaded kernel_mul_mat_f16_f32                0x1147c0230\n",
      "ggml_metal_init: loaded kernel_mul_mat_q4_0_f32               0x1147c0490\n",
      "ggml_metal_init: loaded kernel_mul_mat_q4_1_f32               0x1147c06f0\n",
      "ggml_metal_init: loaded kernel_mul_mat_q2_k_f32               0x1147c0950\n",
      "ggml_metal_init: loaded kernel_mul_mat_q3_k_f32               0x1147c0bb0\n",
      "ggml_metal_init: loaded kernel_mul_mat_q4_k_f32               0x1147c0e10\n",
      "ggml_metal_init: loaded kernel_mul_mat_q5_k_f32               0x1147c1070\n",
      "ggml_metal_init: loaded kernel_mul_mat_q6_k_f32               0x1147c13d0\n",
      "ggml_metal_init: loaded kernel_rope                           0x1147c1a00\n",
      "ggml_metal_init: loaded kernel_alibi_f32                      0x1147c2120\n",
      "ggml_metal_init: loaded kernel_cpy_f32_f16                    0x115fd1690\n",
      "ggml_metal_init: loaded kernel_cpy_f32_f32                    0x115fd1c60\n",
      "ggml_metal_init: loaded kernel_cpy_f16_f16                    0x115fd2d40\n",
      "ggml_metal_init: recommendedMaxWorkingSetSize = 21845.34 MB\n",
      "ggml_metal_init: hasUnifiedMemory             = true\n",
      "ggml_metal_init: maxTransferRate              = built-in GPU\n",
      "ggml_metal_add_buffer: allocated 'data            ' buffer, size =  6984.06 MB, ( 6984.45 / 21845.34)\n",
      "ggml_metal_add_buffer: allocated 'eval            ' buffer, size =  1024.00 MB, ( 8008.45 / 21845.34)\n",
      "ggml_metal_add_buffer: allocated 'kv              ' buffer, size =  1602.00 MB, ( 9610.45 / 21845.34)\n",
      "ggml_metal_add_buffer: allocated 'scr0            ' buffer, size =   512.00 MB, (10122.45 / 21845.34)\n",
      "ggml_metal_add_buffer: allocated 'scr1            ' buffer, size =   512.00 MB, (10634.45 / 21845.34)\n"
     ]
    }
   ],
   "source": [
    "from langchain_community.llms import GPT4All\n",
    "\n",
    "gpt4all = GPT4All(\n",
    "    model=\"/Users/rlm/Desktop/Code/gpt4all/models/nous-hermes-13b.ggmlv3.q4_0.bin\",\n",
    "    max_tokens=2048,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6d012e4-0eef-4734-a826-89ec74fe9f88",
   "metadata": {},
   "source": [
    "### llamafile\n",
    "\n",
    "One of the simplest ways to run an LLM locally is using a [llamafile](https://github.com/Mozilla-Ocho/llamafile). All you need to do is:\n",
    "\n",
    "1) Download a llamafile from [HuggingFace](https://huggingface.co/models?other=llamafile)\n",
    "2) Make the file executable\n",
    "3) Run the file\n",
    "\n",
    "llamafiles bundle model weights and a [specially-compiled](https://github.com/Mozilla-Ocho/llamafile?tab=readme-ov-file#technical-details) version of [`llama.cpp`](https://github.com/ggerganov/llama.cpp) into a single file that can run on most computers without any additional dependencies. They also come with an embedded inference server that provides an [API](https://github.com/Mozilla-Ocho/llamafile/blob/main/llama.cpp/server/README.md#api-endpoints) for interacting with your model. \n",
    "\n",
    "Here's a simple bash script that shows all 3 setup steps:\n",
    "\n",
    "```bash\n",
    "# Download a llamafile from HuggingFace\n",
    "wget https://huggingface.co/jartine/TinyLlama-1.1B-Chat-v1.0-GGUF/resolve/main/TinyLlama-1.1B-Chat-v1.0.Q5_K_M.llamafile\n",
    "\n",
    "# Make the file executable. On Windows, instead just rename the file to end in \".exe\".\n",
    "chmod +x TinyLlama-1.1B-Chat-v1.0.Q5_K_M.llamafile\n",
    "\n",
    "# Start the model server. Listens at http://localhost:8080 by default.\n",
    "./TinyLlama-1.1B-Chat-v1.0.Q5_K_M.llamafile --server --nobrowser\n",
    "```\n",
    "\n",
    "After you run the above setup steps, you can interact with the model via LangChain:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "735e45b6-9aff-463e-aae4-bbf8ac2b21c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\n-1 1/2 (8 oz. Pounds) ground beef, browned and cooked until no longer pink\\n-3 cups whole wheat spaghetti\\n-4 (10 oz) cans diced tomatoes with garlic and basil\\n-2 eggs, beaten\\n-1 cup grated parmesan cheese\\n-1/2 teaspoon salt\\n-1/4 teaspoon black pepper\\n-1 cup breadcrumbs (16 oz)\\n-2 tablespoons olive oil\\n\\nInstructions:\\n1. Cook spaghetti according to package directions. Drain and set aside.\\n2. In a large skillet, brown ground beef over medium heat until no longer pink. Drain any excess grease.\\n3. Stir in diced tomatoes with garlic and basil, and season with salt and pepper. Cook for 5 to 7 minutes or until sauce is heated through. Set aside.\\n4. In a large bowl, beat eggs with a fork or whisk until fluffy. Add cheese, salt, and black pepper. Set aside.\\n5. In another bowl, combine breadcrumbs and olive oil. Dip each spaghetti into the egg mixture and then coat in the breadcrumb mixture. Place on baking sheet lined with parchment paper to prevent sticking. Repeat until all spaghetti are coated.\\n6. Heat oven to 375 degrees. Bake for 18 to 20 minutes, or until lightly golden brown.\\n7. Serve hot with meatballs and sauce on the side. Enjoy!'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from langchain_community.llms.llamafile import Llamafile\n",
    "\n",
    "llamafile = Llamafile()\n",
    "\n",
    "llamafile.invoke(\"Here is my grandmother's beloved recipe for spaghetti and meatballs:\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d58838ae",
   "metadata": {},
   "source": [
    "## Using in a chain\n",
    "\n",
    "We can create a summarization chain with either model by passing in the retrieved docs and a simple prompt.\n",
    "\n",
    "It formats the prompt template using the input key values provided and passes the formatted string to `GPT4All`, `LLama-V2`, or another specified LLM."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "18a3716d",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Llama.generate: prefix-match hit\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Based on the retrieved documents, the main themes are:\n",
      "1. Task decomposition: The ability to break down complex tasks into smaller subtasks, which can be handled by an LLM or other components of the agent system.\n",
      "2. LLM as the core controller: The use of a large language model (LLM) as the primary controller of an autonomous agent system, complemented by other key components such as a knowledge graph and a planner.\n",
      "3. Potentiality of LLM: The idea that LLMs have the potential to be used as powerful general problem solvers, not just for generating well-written copies but also for solving complex tasks and achieving human-like intelligence.\n",
      "4. Challenges in long-term planning: The challenges in planning over a lengthy history and effectively exploring the solution space, which are important limitations of current LLM-based autonomous agent systems."
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "llama_print_timings:        load time =  1191.88 ms\n",
      "llama_print_timings:      sample time =   134.47 ms /   193 runs   (    0.70 ms per token,  1435.25 tokens per second)\n",
      "llama_print_timings: prompt eval time = 39470.18 ms /  1055 tokens (   37.41 ms per token,    26.73 tokens per second)\n",
      "llama_print_timings:        eval time =  8090.85 ms /   192 runs   (   42.14 ms per token,    23.73 tokens per second)\n",
      "llama_print_timings:       total time = 47943.12 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'\\nBased on the retrieved documents, the main themes are:\\n1. Task decomposition: The ability to break down complex tasks into smaller subtasks, which can be handled by an LLM or other components of the agent system.\\n2. LLM as the core controller: The use of a large language model (LLM) as the primary controller of an autonomous agent system, complemented by other key components such as a knowledge graph and a planner.\\n3. Potentiality of LLM: The idea that LLMs have the potential to be used as powerful general problem solvers, not just for generating well-written copies but also for solving complex tasks and achieving human-like intelligence.\\n4. Challenges in long-term planning: The challenges in planning over a lengthy history and effectively exploring the solution space, which are important limitations of current LLM-based autonomous agent systems.'"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from langchain_core.output_parsers import StrOutputParser\n",
    "from langchain_core.prompts import PromptTemplate\n",
    "\n",
    "# Prompt\n",
    "prompt = PromptTemplate.from_template(\n",
    "    \"Summarize the main themes in these retrieved docs: {docs}\"\n",
    ")\n",
    "\n",
    "\n",
    "# Chain\n",
    "def format_docs(docs):\n",
    "    return \"\\n\\n\".join(doc.page_content for doc in docs)\n",
    "\n",
    "\n",
    "chain = {\"docs\": format_docs} | prompt | llm | StrOutputParser()\n",
    "\n",
    "# Run\n",
    "question = \"What are the approaches to Task Decomposition?\"\n",
    "docs = vectorstore.similarity_search(question)\n",
    "chain.invoke(docs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3cce6977-52e7-4944-89b4-c161d04f6698",
   "metadata": {},
   "source": [
    "## Q&A \n",
    "\n",
    "We can also use the LangChain Prompt Hub to store and fetch prompts that are model-specific.\n",
    "\n",
    "Let's try with a default RAG prompt, [here](https://smith.langchain.com/hub/rlm/rag-prompt)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "59ed5f0d-7089-41cc-8486-af37b690dd33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[HumanMessagePromptTemplate(prompt=PromptTemplate(input_variables=['context', 'question'], template=\"You are an assistant for question-answering tasks. Use the following pieces of retrieved context to answer the question. If you don't know the answer, just say that you don't know. Use three sentences maximum and keep the answer concise.\\nQuestion: {question} \\nContext: {context} \\nAnswer:\"))]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from langchain import hub\n",
    "\n",
    "rag_prompt = hub.pull(\"rlm/rag-prompt\")\n",
    "rag_prompt.messages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "c01c1725",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Llama.generate: prefix-match hit\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Hi there! There are three main approaches to task decomposition. One is using LLM with simple prompting like \"Steps for XYZ.\" or \"What are the subgoals for achieving XYZ?\" Another approach is by using task-specific instructions, such as \"Write a story outline\" for writing a novel. Finally, task decomposition can also be done with human inputs. Thanks for asking!"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "llama_print_timings:        load time =  1191.88 ms\n",
      "llama_print_timings:      sample time =    61.21 ms /    85 runs   (    0.72 ms per token,  1388.64 tokens per second)\n",
      "llama_print_timings: prompt eval time =  8014.11 ms /   267 tokens (   30.02 ms per token,    33.32 tokens per second)\n",
      "llama_print_timings:        eval time =  2908.17 ms /    84 runs   (   34.62 ms per token,    28.88 tokens per second)\n",
      "llama_print_timings:       total time = 11096.23 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'output_text': ' Hi there! There are three main approaches to task decomposition. One is using LLM with simple prompting like \"Steps for XYZ.\" or \"What are the subgoals for achieving XYZ?\" Another approach is by using task-specific instructions, such as \"Write a story outline\" for writing a novel. Finally, task decomposition can also be done with human inputs. Thanks for asking!'}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from langchain_core.runnables import RunnablePassthrough, RunnablePick\n",
    "\n",
    "# Chain\n",
    "chain = (\n",
    "    RunnablePassthrough.assign(context=RunnablePick(\"context\") | format_docs)\n",
    "    | rag_prompt\n",
    "    | llm\n",
    "    | StrOutputParser()\n",
    ")\n",
    "\n",
    "# Run\n",
    "chain.invoke({\"context\": docs, \"question\": question})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e5913f0-cf92-4e21-8794-0502ba11b202",
   "metadata": {},
   "source": [
    "Now, let's try with [a prompt specifically for LLaMA](https://smith.langchain.com/hub/rlm/rag-prompt-llama), which [includes special tokens](https://huggingface.co/blog/llama2#how-to-prompt-llama-2)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "78f6862d-b7a6-4e03-84e4-45667185bf9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ChatPromptTemplate(input_variables=['question', 'context'], output_parser=None, partial_variables={}, messages=[HumanMessagePromptTemplate(prompt=PromptTemplate(input_variables=['question', 'context'], output_parser=None, partial_variables={}, template=\"[INST]<<SYS>> You are an assistant for question-answering tasks. Use the following pieces of retrieved context to answer the question. If you don't know the answer, just say that you don't know. Use three sentences maximum and keep the answer concise.<</SYS>> \\nQuestion: {question} \\nContext: {context} \\nAnswer: [/INST]\", template_format='f-string', validate_template=True), additional_kwargs={})])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Prompt\n",
    "rag_prompt_llama = hub.pull(\"rlm/rag-prompt-llama\")\n",
    "rag_prompt_llama.messages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "67cefb46-acd3-4c2a-a8f6-b62c7c3e30dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Llama.generate: prefix-match hit\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Sure, I'd be happy to help! Based on the context, here are some to task:\n",
      "\n",
      "1. LLM with simple prompting: This using a large model (LLM) with simple prompts like \"Steps for XYZ\" or \"What are the subgoals for achieving XYZ?\" to decompose tasks into smaller steps.\n",
      "2. Task-specific: Another is to use task-specific, such as \"Write a story outline\" for writing a novel, to guide the of tasks.\n",
      "3. Human inputs:, human inputs can be used to supplement the process, in cases where the task a high degree of creativity or expertise.\n",
      "\n",
      "As fores in long-term and task, one major is that LLMs to adjust plans when faced with errors, making them less robust to humans who learn from trial and error."
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "llama_print_timings:        load time = 11326.20 ms\n",
      "llama_print_timings:      sample time =   144.81 ms /   207 runs   (    0.70 ms per token,  1429.47 tokens per second)\n",
      "llama_print_timings: prompt eval time =  1506.13 ms /   258 tokens (    5.84 ms per token,   171.30 tokens per second)\n",
      "llama_print_timings:        eval time =  6231.92 ms /   206 runs   (   30.25 ms per token,    33.06 tokens per second)\n",
      "llama_print_timings:       total time =  8158.41 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'output_text': '  Sure, I\\'d be happy to help! Based on the context, here are some to task:\\n\\n1. LLM with simple prompting: This using a large model (LLM) with simple prompts like \"Steps for XYZ\" or \"What are the subgoals for achieving XYZ?\" to decompose tasks into smaller steps.\\n2. Task-specific: Another is to use task-specific, such as \"Write a story outline\" for writing a novel, to guide the of tasks.\\n3. Human inputs:, human inputs can be used to supplement the process, in cases where the task a high degree of creativity or expertise.\\n\\nAs fores in long-term and task, one major is that LLMs to adjust plans when faced with errors, making them less robust to humans who learn from trial and error.'}"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Chain\n",
    "chain = (\n",
    "    RunnablePassthrough.assign(context=RunnablePick(\"context\") | format_docs)\n",
    "    | rag_prompt_llama\n",
    "    | llm\n",
    "    | StrOutputParser()\n",
    ")\n",
    "\n",
    "# Run\n",
    "chain.invoke({\"context\": docs, \"question\": question})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "821729cb",
   "metadata": {},
   "source": [
    "## Q&A with retrieval\n",
    "\n",
    "Instead of manually passing in docs, we can automatically retrieve them from our vector store based on the user question.\n",
    "\n",
    "This will use a QA default prompt (shown [here](https://github.com/langchain-ai/langchain/blob/275b926cf745b5668d3ea30236635e20e7866442/langchain/chains/retrieval_qa/prompt.py#L4)) and will retrieve from the vectorDB."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "86c7a349",
   "metadata": {},
   "outputs": [],
   "source": [
    "retriever = vectorstore.as_retriever()\n",
    "qa_chain = (\n",
    "    {\"context\": retriever | format_docs, \"question\": RunnablePassthrough()}\n",
    "    | rag_prompt\n",
    "    | llm\n",
    "    | StrOutputParser()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "112ca227",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Llama.generate: prefix-match hit\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " \n",
      "The three approaches to Task decomposition are LLMs with simple prompting, task-specific instructions, or human inputs. Thanks for asking!"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "llama_print_timings:        load time =  1191.88 ms\n",
      "llama_print_timings:      sample time =    22.78 ms /    31 runs   (    0.73 ms per token,  1360.66 tokens per second)\n",
      "llama_print_timings: prompt eval time =     0.00 ms /     1 tokens (    0.00 ms per token,      inf tokens per second)\n",
      "llama_print_timings:        eval time =  1320.23 ms /    31 runs   (   42.59 ms per token,    23.48 tokens per second)\n",
      "llama_print_timings:       total time =  1387.70 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'query': 'What are the approaches to Task Decomposition?',\n",
       " 'result': ' \\nThe three approaches to Task decomposition are LLMs with simple prompting, task-specific instructions, or human inputs. Thanks for asking!'}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qa_chain.invoke(question)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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